We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| AnkurMali/LRA-E | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy_function | Ran | AnkurMali/LRA-E/fmnist/fmnist_newTF.py code served (permissive licence) · get_code("eb3d9997cc24c7ee") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research. In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery. In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as LRA-E. We compare our procedures to several other biologically-motivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation. In two benchmarks, we find that both of our proposed algorithms yield stable performance and strong generalization compared to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1805.11703")
get_code_for_paper("1805.11703")
have("1805.11703")
Connect an agent — have() is free.